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On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models : Moving block bootstrap inference under weak identification

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On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models : Moving block bootstrap inference under weak identification

Content Provider	Springer Nature Link
Author	Ouysse, Rachida
Copyright Year	2013
Abstract	In the context of the continuously updated generalized-methods-of-moments (GMM), this study evaluates the finite sample properties of Wald- and criterion-based bootstrap inference for a class of models defined by non-linear conditional moment functions. This work provides simulation evidence that validates the moving block-bootstrap (MBB) as an alternative to asymptotic approximation for robust finite sample GMM inference. The study considers data generating processes with highly non-linear conditional moment functions, weak instruments, and near failure of the identification condition. In the absence of a consensus on best practice when identification is weak, Monte Carlo results of this study are encouraging to the empirical researchers. For criterion-based tests, the MBB performs fairly well in reducing the error in the rejection frequency that occurs when first-order asymptotic critical values are used. In particular, it is possible to improve finite sample inference by inverting bootstrap Wald-type statistics which are commonly used in practice The bootstrap percentile- $$t$$ confidence intervals performed better than the asymptotic confidence intervals but only marginally in weakly identified specifications with high non-linear moment functions.
Starting Page	233
Ending Page	261
Page Count	29
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	29
Issue Number	1-2
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Continuously-updated GMM Moving block bootstrap Size and power of a test Monte Carlo test Criterion-based test Consumption-based capital asset pricing model Fast double bootstrap approximation Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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